2018 •
Updated astrometry and masses of the LUH 16 brown dwarf binary
Authors:
P. F. Lazorenko, J. Sahlmann
Abstract:
The nearest known binary brown dwarf WISE J104915.57-531906.1AB (LUH 16) is a well-studied benchmark for our understanding of substellar objects. Previously published astrometry of LUH 16 obtained with FORS2 on the Very Large Telescope was affected by errors that limited its use in combination with other datasets, thereby hampering the determination of its accurate orbital parameters and masses. We improve upon the calibration and analysis of the FORS2 astrometry with the help of Gaia DR2 to generate a high-precision dataset that can be combine (...)
The nearest known binary brown dwarf WISE J104915.57-531906.1AB (LUH 16) is a well-studied benchmark for our understanding of substellar objects. Previously published astrometry of LUH 16 obtained with FORS2 on the Very Large Telescope was affected by errors that limited its use in combination with other datasets, thereby hampering the determination of its accurate orbital parameters and masses. We improve upon the calibration and analysis of the FORS2 astrometry with the help of Gaia DR2 to generate a high-precision dataset that can be combined with present and future LUH 16 astrometry. We demonstrate its use by combining it with available measurements from the Hubble Space Telescope (HST) and Gemini/GeMS and deriving updated orbital and mass parameters. Using Gaia DR2 as astrometric reference field, we derived the absolute proper motion and updated the absolute parallax of the binary to 501.557 +/- 0.082 mas. We refined the individual dynamical masses of LUH 16 to 33.5 +/- 0.3 M[Jupiter] (component A) and 28.6 +/- 0.3 M[Jupiter] (component B), which corresponds to a relative precision of ~1% and is three to four times more precise than previous estimates. We found that these masses show a weak dependence on one datapoint extracted from a photographic plate from 1984. The exact determination of a residual mass bias, if any, will be possible when more high-precision data can be incorporated in the analysis. (Read More)
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